The innovation of our system is based on five pillars, which may be briefly described as follows:

• The first pillar is the integration of different platforms and understanding of recovered data. The data themselves are not necessarily useful or may not yet show their full potential. That is why it is crucial to understand the connections and the logical meaning of the information downloaded. This is a very tedious task that requires a good level of business and data understanding.

• The second pillar has a standardized internal data model that is comprehensive enough to support various inbound formats (retrieved using the first pillar) while maintaining the basic data structure.

• The third pillar is the logical continuation of the first two pillars, it is a data quality check. This is a particularly important task because some attributes collected from different sources of information often describe the same thing, but use a different descriptor. To ensure a level of data quality and standards, several automated processes are responsible. These include tasks such as synchronizing different types of text data based on mapping tables to a standard value.